Gemini 3.1 Pro Preview vs Llama 3.3 70B Instruct

In our 12-test benchmark suite (scored 1–5), Gemini 3.1 Pro Preview wins 8 tests, Llama 3.3 70B Instruct wins 1, and 3 are tied.

Where Gemini 3.1 Pro Preview wins:

• Structured output: 5 vs 4. Gemini ties for 1st among 54 models; Llama ranks 26th. For APIs requiring strict JSON schema compliance, this gap is operationally significant.
• Strategic analysis: 5 vs 3. Gemini ties for 1st among 54 models; Llama ranks 36th. A full two-point gap means meaningfully better nuanced tradeoff reasoning with real numbers.
• Creative problem solving: 5 vs 3. Gemini ties for 1st among 54 models; Llama ranks 30th. Non-obvious, feasible idea generation is a strength Llama doesn't approach here.
• Faithfulness: 5 vs 4. Gemini ties for 1st among 55 models; Llama ranks 34th. Lower hallucination risk when summarizing or citing source material.
• Persona consistency: 5 vs 3. Gemini ties for 1st among 53 models; Llama ranks 45th. Important for chatbot and roleplay applications requiring character stability.
• Agentic planning: 5 vs 3. Gemini ties for 1st among 54 models; Llama ranks 42nd. Goal decomposition and failure recovery are critical for autonomous agent workflows — this gap is large.
• Multilingual: 5 vs 4. Gemini ties for 1st among 55 models; Llama ranks 36th. Non-English output quality is noticeably stronger.
• Constrained rewriting: 4 vs 3. Gemini ranks 6th of 53; Llama ranks 31st. Compression within hard character limits favors Gemini.

Where Llama 3.3 70B Instruct wins:

• Classification: 4 vs 2. Llama ties for 1st among 53 models; Gemini ranks 51st — near the bottom. This is Gemini's clearest weakness and a genuine differentiator for routing and categorization pipelines.

Ties (same score):

• Tool calling: Both score 4/5, both rank 18th of 54. Neither has an edge for function calling workflows.
• Long context: Both score 5/5, both tied for 1st of 55. Both handle 30K+ token retrieval equally well (though Gemini's context window is 1,048,576 tokens vs Llama's 131,072 — a massive difference if your use case involves very long documents).
• Safety calibration: Both score 2/5, both rank 12th of 55. Neither model distinguishes itself on refusing harmful requests while permitting legitimate ones.

External benchmarks (Epoch AI): On AIME 2025, Gemini 3.1 Pro Preview scores 95.6% (rank 2 of 23 models tested), while Llama 3.3 70B Instruct scores 5.1% (rank 23 of 23 — last place). This is a stark gap in advanced mathematical reasoning. Llama 3.3 70B Instruct also scores 41.6% on MATH Level 5 (rank 14 of 14 — last place among models with scores in our dataset), confirming that competition-level math is a significant weakness. Gemini's reasoning token support (noted in its quirks) likely contributes to this advantage.

Gemini 3.1 Pro Preview costs $2.00 per million input tokens and $12.00 per million output tokens. Llama 3.3 70B Instruct costs $0.10 per million input tokens and $0.32 per million output tokens — a 20x input gap and 37.5x output gap.

At 1M output tokens/month: Gemini costs $12, Llama costs $0.32 — a $11.68 difference that most projects absorb easily.

At 10M output tokens/month: $120 vs $3.20. Still manageable for teams, but the gap grows meaningful.

At 100M output tokens/month: $1,200 vs $32. At this scale, the $1,168 monthly delta is a real budget line item, and Llama's lower capability ceiling may be acceptable depending on the task mix.

Developers running classification pipelines, routing layers, or high-frequency summarization tasks should strongly consider Llama 3.3 70B Instruct — it matches Gemini on classification (4/5 vs 2/5, Llama actually wins here) and costs a fraction of the price. But for agentic workflows, reasoning-heavy tasks, or multimodal inputs, Gemini 3.1 Pro Preview's performance advantage likely justifies the premium. Gemini also supports image, audio, video, and file inputs — Llama 3.3 70B Instruct is text-only — which can eliminate the need for a separate vision model and offset some cost.

Choose Gemini 3.1 Pro Preview if:

• You need agentic workflows — it scores 5/5 vs Llama's 3/5 on agentic planning in our tests, with dramatically better goal decomposition and recovery.
• Math or reasoning is central to your use case — 95.6% vs 5.1% on AIME 2025 (Epoch AI) is not a close race.
• You process multimodal inputs (images, audio, video, files) — Llama 3.3 70B Instruct is text-only.
• You need faithfulness to source material — 5/5 vs 4/5, with Gemini ranking 1st vs Llama's 34th of 55.
• Your documents exceed 131K tokens — Gemini's 1M+ token context window has no peer here.
• Strategic analysis, creative problem solving, or persona-driven applications are your primary workload.

Choose Llama 3.3 70B Instruct if:

• Classification and routing are your dominant task — it scores 4/5 (tied 1st of 53) vs Gemini's 2/5 (rank 51 of 53). For a model you're paying 37.5x less for to win on your primary benchmark is a clear signal.
• You're running at 10M+ output tokens/month and the $1,100+ monthly savings per 100M tokens materially impacts your unit economics.
• Your workflow is text-only and doesn't require reasoning tokens, multimodal inputs, or long-context beyond 128K.
• You want sampling control — Llama supports top_k, min_p, logprobs, repetition_penalty, and logit_bias, which Gemini does not expose per the payload.